model-selection 0.0.1

A Library which provides more information about suitable Machine learning algorithm for your dataset

Model Selection

Scope of This Project

• As a beginner in Data science/Machine learning field, most of us having issue in Feature Selection, Feature Extraction and Model Selection. Algorithm-Finder can help you to solve this problem. using Algorithm-Finder you can achieve the below tasks and more.

  • Model Selection
  • Feature Selection
  • Feature Extraction
  • Optimized tuning parameters

• This package mainly used scikit-learn for most of the estimators, by using Algorithm-Finder you can apply your dataset on below models

  • ALL --> ALL IN
  • MLR --> MultiLinearRegression
  • POLY --> PolynomialRegression
  • SVR --> SupportVectorRegression
  • DTREE --> DecisionTreeRegression & DecisionTreeClassification
  • RFR --> RandomForestRegression
  • SIGMOID --> LogisticRegression
  • KNN --> K-NearestNeighbours Classifier
  • SVM --> SupportVectorMachine Classifier
  • RFC --> RandomForestClassifier
  • BAYESIAN --> NaiveBayesClassifier